If only the Walibi amusement park had the power to see into the rollercoaster’s infrastructure—helping them to find the root cause quickly to resolve the incident faster—their customers would have had a far better experience. Speaking of using infrastructure monitoring to provide an excellent customer experience, here’s the latest in AIOps, ITOps, and IT infrastructure monitoring.
1. Combining AIOps and DevOps may help increase efficiency.
According to this article in 7wdata.com, DevOps can help companies put machine learning (ML) and artificial intelligence (AI) to work and help engineers who are trying to build robust apps. The model should be adjusted to the specifics of these advanced fields and take into consideration the assortment of stakeholders in both ML and AI. And you should consider that some individuals will come from data and statistics and business operations. Then add in software engineers, IT support, and end users. AIOps continues the DevOps revolution. DevOps is driven by cloud services and the increasing orchestration of software. While AIOps enables companies to bring ML and AI to fruition, solving ITOps challenges such as capacity management and reliability. Lastly, AIOps increases automation—already an essential part of DevOps.
2. Especially during the pandemic, AIOps can help companies operating in the digital environment to be vigilant when it comes to network operations.
According to this article in DevOps.com, applications of AIOps amid the COVID-19 outbreak can pave the roads for leading challenges. The rapid digital transformations during the pandemic are giving birth to various challenges with respect to security, user experience, and the downfall of industries. How? Because performance attributes such as data analysis and monitoring for controlled access are in high demand. Advanced automation techniques have become possible with AIOps, helping undergo operations in efficiency and eliminating the time-consuming and frustrating manual tasks. Machine learning and artificial intelligence algorithms collectively help reduce the overall response time, provide optimal user experience, and protect network systems. Businesses are also revamping operational infrastructure and fraudsters are actively exploiting vulnerabilities in the digital environment. The density with which the fraud is increasing each passing day is high. AIOps, under these circumstances, can help businesses take into consideration vigilant security measures. The innovative solutions employ AI algorithms for the security of online platforms
3. ITOps personnel will have to adopt new skill sets in an AIOps world.
According to this article in 7wdata.com, just as data centers have evolved using new technologies, ITOps teams should also evolve by learning and using new skills to manage AIOps. Traditional ITOps work focuses on producing and maintaining consistent, stable environments for service and application delivery. These tools also try and provide useful information for the execution of these tasks. Generally, these tools use human domain knowledge or analytic techniques. On the other hand, AIOps uses big data, algorithms, and ML to examine the profile of IT and business data in order to determine what “normal” looks like, find what factors are causal and correlative when things aren’t normal, and automatically recommend or implement a response. Machines execute these steps at incredibly fast rates on exponentially increasing amounts of data. With AIOps, ITOps job skills expand to include auditing AIOps results. ITOps will need to understand how and why the AIOps platform is producing the outcomes it’s recommending or implementing. In an AIOps environment, ITOps personnel need an enhanced skill set that helps them oversee the machine’s work, rather than just performing the work themselves.
4. The latest release of the open-source container-orchestration system Kubernetes will be here soon.
According to ITOps Times, the latest Kubernetes release (1.19) is expected to have 34 new enhancements with 10 brand new features, eight newly stable features, two management changes, and 14 improvements to existing features. The 1.19 release will also implement a warning mechanism for deprecated features which will help alert developers and cluster admins that a feature is deprecating soon in order to help them plan ahead. More features will make things easier for users to include enhancements on how beta features are handled and graduated to stable and generic ephemeral inline volumes.
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